Suchergebnisse - adversarial autoencoder

Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings von Dai, Yuanfei, Guo, Chenhao, Guo, Wenzhong, Eickhoff, Carsten

“… As a consequence, these samples are often too simplistic to train an effective model. In this paper, we propose a new KG embedding framework by introducing adversarial autoencoders (AAEs …”
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Reducing variations in multi-center Alzheimer’s disease classification with convolutional adversarial autoencoder von Cobbinah, Bernard M., Sorg, Christian, Yang, Qinli, Ternblom, Arvid, Zheng, Changgang, Han, Wei, Che, Liwei, Shao, Junming

“… Based on brain magnetic resonance imaging (MRI), multiple variations ranging from MRI scanners to center-specific parameter settings, imaging protocols, and …”
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Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non‐Gaussian Hydraulic Conductivities von Mo, Shaoxing, Zabaras, Nicholas, Shi, Xiaoqing, Wu, Jichun

“… In this study, we develop a convolutional adversarial autoencoder (CAAE) to parameterize non‐Gaussian conductivity fields with heterogeneous conductivity within each facies using a low …”
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Advancing Speech Enhancement with Generative Adversarial Network-Autoencoder: A Robust Adversarial Autoencoder Approach von Diwakar, Mandar, Gupta, Brijedra

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Continuous Latent Adversarial Autoencoder: A Time-Sensitive Method for Incomplete Time-Series Modeling von Chang, Zhuoqing, Liu, Shubo, Cai, Zhaohui, Tu, Guoqing

“… To address these issues, this article introduces a novel generative model called the continuous latent adversarial autoencoder (CLAAE …”
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RETRACTION: DAE‐GAN: An Autoencoder Based Adversarial Network for Gaussian Denoising

“… RETRACTION: A. Samanta, A. Saha, S. C. Satapathy, H. Lin, “DAE‐GAN: An Autoencoder Based Adversarial Network for Gaussian Denoising,” Expert Systems (Early View): e12709, https://doi.org/10.1111/exsy.12709 …”
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Unified multi-protocol MRI for Alzheimer’s disease diagnosis: Dual-decoder adversarial autoencoder and ensemble residual shrinkage attention network von Li, Shiyao, Lin, Shukuan, Tu, Yue, Qiao, Jianzhong, Xiao, Shenao

“… Specifically, we introduce a double decoder adversarial autoencoder (DDAAE) to align MRIs from different protocols …”
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Publisher's Note: "Deep network fault diagnosis for imbalanced small-sized samples via a coupled adversarial autoencoder based on the Bayesian method" [Rev. Sci. Instrum. 95, 055104 (2024)] von Zhang, Xinliang, Wang, Yanqi, Zhou, Yitian, Jia, Lijie

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Denoising Adversarial Autoencoders von Creswell, Antonia, Bharath, Anil Anthony

“… We suggest denoising adversarial autoencoders (AAEs), which combine denoising and regularization, shaping the distribution of latent space using …”
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Lifelong Generative Adversarial Autoencoder von Ye, Fei, Bors, Adrian G.

“… Lifelong learning describes an ability that enables humans to continually acquire and learn new information without forgetting. This capability, common to …”
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Adversarial Autoencoder Network for Hyperspectral Unmixing von Jin, Qiwen, Ma, Yong, Fan, Fan, Huang, Jun, Mei, Xiaoguang, Ma, Jiayi

“… Since the unmixing procedure can be explained as finding a set of low-dimensional representations that reconstruct the data with their corresponding bases, autoencoders (AEs …”
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Age Progression/Regression by Conditional Adversarial Autoencoder von Zhifei Zhang, Yang Song, Hairong Qi

“… If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing …”
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Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery von Polykovskiy, Daniil, Zhebrak, Alexander, Vetrov, Dmitry, Ivanenkov, Yan, Aladinskiy, Vladimir, Mamoshina, Polina, Bozdaganyan, Marine, Aliper, Alexander, Zhavoronkov, Alex, Kadurin, Artur

“… In this article, we propose a new generative architecture, entangled conditional adversarial autoencoder, that generates molecular structures based on various properties, such as activity against …”
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A Novel Fault Detection Method Based on One-Dimension Convolutional Adversarial Autoencoder (1DAAE) von Wang, Jian, Li, Yakun, Han, Zhiyan

“… To deal with this problem, this paper proposes a novel unsupervised fault detection method named one-dimension convolutional adversarial autoencoder (1DAAE …”
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Learning to Generate SAR Images With Adversarial Autoencoder von Song, Qian, Xu, Feng, Zhu, Xiao Xiang, Jin, Ya-Qiu

“… A novel adversarial autoencoder (AAE) is then proposed as an SAR representation and generation network …”
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Causal Adversarial Autoencoder for Disentangled SAR Image Representation and Few-Shot Target Recognition von Guo, Qian, Xu, Huilin, Xu, Feng

“… A Causal Adversarial auto-Encoder (CAE) for SAR-ATR is then proposed to embody this disentangled representation, which incorporates a number of novel built-in network features …”
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Center-Aware Adversarial Autoencoder for Anomaly Detection von Li, Daoming, Tao, Qinghua, Liu, Jiahao, Wang, Huangang

“… To this end, we propose a center-aware adversarial autoencoder (CA-AAE) method, which detects anomaly samples by acquiring more compact and discriminative subspace representations …”
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Fault-Attention Generative Probabilistic Adversarial Autoencoder for Machine Anomaly Detection von Wu, Jingyao, Zhao, Zhibin, Sun, Chuang, Yan, Ruqiang, Chen, Xuefeng

“… In this article, anomaly detection is modeled as a one-class classification problem. Based on the scenario that the training data only include healthy state data, a fault-attention generative probabilistic adversarial autoencoder (FGPAA …”
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Generating sequential electronic health records using dual adversarial autoencoder von Lee, Dongha, Yu, Hwanjo, Jiang, Xiaoqian, Rogith, Deevakar, Gudala, Meghana, Tejani, Mubeen, Zhang, Qiuchen, Xiong, Li

“… We propose a dual adversarial autoencoder (DAAE), which learns set-valued sequences of medical entities, by combining a recurrent autoencoder with 2 generative adversarial networks (GANs …”
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Dual Adversarial Autoencoders for Clustering von Ge, Pengfei, Ren, Chuan-Xian, Dai, Dao-Qing, Feng, Jiashi, Yan, Shuicheng

“… Recently, adversarial autoencoder (AE) (AAE) shows effectiveness on tackling such data by combining AE and adversarial training, but it cannot effectively extract classification information from the unlabeled data …”
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